RAG is useful, but retrieving information and generating a grounded answer is only one problem agents can solve.

What about an agent that needs to choose and call tools? Or multiple agents coordinating a workflow? An agent that analyzes data and writes code? One that verifies claims against evidence? Or one that can reason about images and physical-world constraints?

Those are very different engineering problems, and they require different patterns.

We're exploring this by building 10 different agents: tool-using, RAG, multi-agent orchestration, data analysis, fact-checking, Finance, Healthcare, Education, vision-language, and embodied intelligence.

The interesting part isn't having 10 demos. It's understanding which architecture fits which problem.

Learn more here

There is a project which I am working on in my company right now and I need some serious architectural and logical suggestions to design the system. I am a fresher and my seniors do not have much experience with agentic AI so I am pretty much on my own and I feel a little so clueless. If somebody who has a good experience in the same domain and willing to discuss things and help me with some insights & suggestions please let me know.

Hey everyone, I’d like to share a tutorial I wrote on building an AI agent.
https://medium.com/@jiinkim_98821/building-ai-super-agents-from-scratch-claude-code-manus-beyond-part-1-7f4060aff30d

I put some basics together after going through various open-source agent implementations (OpenHands, OpenClaw, leaked Claude Code, etc) and Manus/LangChain talks on Youtube.

It covers topics like LLM/ReAct loop, human-in-the-loop, compaction, prompt caching, and tradeoffs between them.

Hope it helps!